Method and system for producing statistical analysis of medical care information

ABSTRACT

A method and system for producing statistical analysis of medical care information comprises: aggregating medical care providers to a peer group level; aggregating medical care information at the peer group level and at the medical care provider level; computing a statistical analysis, such as performing Pearson&#39;s correlation analysis; and generating peer group level and medical care provider level results utilizing the computed statistical analysis. Also, a method for producing statistical analysis of medical care information for a medical care provider efficiency measurement comprises: applying minimum unit of analysis criteria for medical care providers to be used in statistical analysis; calculating an overall weighted average medical care information measure for each medical care provider; calculating a medical condition-specific medical care information measure for each medical care provider; removing outlier medical care providers from statistical analysis at medical care information level; calculating a statistical analysis to medical care provider efficiency measurement at each medical care information level using a statistical calculation; and selecting statistically related medical care information to identify medical care providers meeting a desired practice pattern.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.14/266,842, filed May 1, 2014, which is a Continuation of U.S. patentapplication Ser. No. 13/621,222, filed Sep. 15, 2012, and issued as U.S.Pat. No. 8,751,263 on Jun. 10, 2014, which is a Continuation of U.S.patent application Ser. No. 12/473,147, filed May 27, 2009, and issuedas U.S. Pat. No. 8,301,464 on Oct. 30, 2012, which claims priority toour provisional patent application entitled “METHOD AND SYSTEM FORANALYZING PHYSICIAN EFFICIENCY SCORES TO IDENTIFY REASONS FORINEFFICIENT AND EFFICIENT PRACTICE PATTERNS”, with Ser. No. 61/082,080,and filed Jul. 18, 2008, all of which are incorporated herein byreference in their entirety.

FIELD OF THE INVENTION

The present invention generally relates to analyzing health careinformation and, more specifically, to a system and method of producingstatistical analysis of medical care information for a medical careprovider efficiency measurement. The method comprises calculating astatistical analysis to medical care provider efficiency measurement atan overall weighted average and at each medical care information levelusing a statistical calculation; and selecting statistically relatedmedical care information to identify medical care providers meeting adesired practice pattern.

BACKGROUND OF THE INVENTION

Health care costs continue to rise at a rapid rate and total nationalhealth expenditures are expected to rise at twice the rate of inflationin 2008. U.S. health care spending is expected to increase at similarlevels for the next decade.

One factor contributing to rising health care costs is due to 10% to 20%of physicians, across specialty types, practicing inefficiently.Efficiency means using an appropriate amount of medical resources in anappropriate setting to treat a medical condition or given number ofmedical conditions, and achieving a desired health outcome and qualityof patient care. Thus, efficiency is a function of unit price, volume ofservice, intensity of service, and quality of service. The inefficientpractitioners are often those 10% to 20% of practitioners by specialtytype utilizing significantly more services to treat a given grouping ofpatients with equivalent medical conditions or condition-specificepisodes of care as compared to their immediate peer group or bestpractice guideline. The inefficient practitioners can be responsible fordriving 10% to 20% of the unnecessary, excess, medical expendituresincurred by employers and other health care purchasers, equating tobillions of dollars nationally.

Currently health plans, insurance companies, third party administrators(TPAs), health maintenance organizations, and other health firms (whichcollectively shall be called “health plans”) expend a significant amountof technical, clinical, and analytical resources trying to identify theinefficient practitioners.

Once health plans have identified inefficient practitioner, they realizethat each practitioner has a different practice pattern to deal with andhas its own little ‘microcosm’ of practice. At the microcosm level, manyclinical and analytical resources are currently expended trying todetermine the microcosm practice patterns for each practitioner for eachspecialty type. The result is that health plans may end up managinghundreds of different practice patterns which is time and resourceintensive and makes monitoring over time difficult.

It is often extremely difficult and costly to identify and target theone or two services most associated with practitioner efficiency.Different practice patterns of each practitioner as well as theinability to easily identify services most associated with practitionerefficiency, make it challenging and costly for health plans to embark onstrategies to reduce expenditure and improve quality. Programs such astargeted practitioner education and behavioral change, Pay forPerformance (P4P) and value-based benefit plan design become moreresource intensive and costly and less effective due to difficulties inknowing where to focus and areas to target for improvements.Additionally, the lack of focus results in challenges in monitoring andmeasuring improvements over time.

BRIEF SUMMARY OF THE INVENTION

A method and system for producing statistical analysis of medical careinformation comprises: aggregating medical care providers to a peergroup level; aggregating medical care information at the peer grouplevel and at the medical care provider level; computing a statisticalanalysis, such as performing Pearson's correlation analysis; andgenerating peer group level and medical care provider level resultsutilizing the computed statistical analysis.

Also, a method for producing statistical analysis of medical careinformation for a medical care provider efficiency measurementcomprises: applying minimum unit of analysis criteria for medical careproviders to be used in statistical analysis; calculating an overallweighted average medical care information measure for each medical careprovider; calculating a medical condition-specific medical careinformation measure for each medical care provider; removing outliermedical care providers from statistical analysis at medical careinformation level; calculating a statistical analysis to medical careprovider efficiency measurement at each medical care information levelusing a statistical calculation; and selecting statistically relatedmedical care information to identify medical care providers meeting adesired practice pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing a Positive Correlation Example;

FIG. 2 is a graph showing a Negative Correlation Example;

FIG. 3 is an exemplary Sub-Service Detail Correlation Report, inaccordance with one embodiment of the present invention;

FIG. 4 shows an exemplary MedMarker Checkout Report, in accordance withthe embodiment shown in FIG. 3;

FIG. 5 shows a MedMarker Target Report, in accordance with theembodiment shown in FIG. 4;

FIG. 6 is an exemplary Practitioner Efficiency Report, in accordancewith one embodiment of the present invention;

FIG. 7 is an exemplary Service Prevalence Report, in accordance with theexample shown in FIG. 6;

FIG. 8 is an exemplary Procedure Code Report for one specialty, inaccordance with the invention and example shown in FIG. 7;

FIGS. 9 and 10 are flowcharts illustrating exemplary operation of oneembodiment of the present invention; and

FIG. 11 is a block diagram illustrating a General Purpose Computer, suchas utilized to implement the present invention, as shown in FIGS. 9 and10.

DETAILED DESCRIPTION OF THE INVENTION

A Grouper system uses medical care information to build medicalcondition-specific episodes. Once these condition-specific episodes ofcare are built, then the episodes are examined through an EfficiencyCaresystem.

Efficiency means using an appropriate amount of medical resources in anappropriate setting to treat a medical condition or a given number ofmedical conditions, and achieve a desired quality of patient care. Thus,efficiency is a function of unit price, volume of service, intensity ofservice, and may include a quality of service component. Volume refersto the number of services performed to treat a specific medicalcondition (e.g., an office visit, two laboratory tests, and oneprescription drug). Intensity refers to the magnitude of medical careordered to treat a medical condition (e.g., an x-ray versus a computedtomography scan).

The end result there is typically a score between 0.70 and 1.50. Thisscore reflects the resources a health care provider uses in treating agrouping of patients with medical conditions or condition-specificepisodes of care as compared to their immediate peer group or a bestpractice guideline. If a health care provider receives a score of 0.70,then that health care provider is using 30% fewer resources as comparedto the peer group.

The Grouper system generates three primary data sets: Assign.tab dataset that assigns episodes of care to health care providers;PatientCLI.tab data set that contains patient claim line items (CLI);and EpMaster.tab data set that contains episodes of care information.The EfficiencyCare system utilizes the Assign.tab data set to generate:a Score.tab data set that includes health care provider efficiencyscores; a Detail.tab data set that provides health care providerefficiency score details; and an ProvEp.tab data set that provideshealth care provider efficiency episodes. The present inventionprimarily involves a BullsEye system that utilizes those data setsdescribed above to generate a BullsEyeMB.tab and BullsEyeMCID.tab dataset that targets medical care information most related to or indicativeof health care provider efficiency and inefficiency.

There are three input files to one embodiment of the present invention.One of these input files comes from the Grouper system, and it's calledthe Patient CLI File 42. This file contains all the claim line itemsfrom the CLI Input File, but with the claims organized by medicalcondition episode of care. In one embodiment, 11 additional pieces ofinformation are added to each claim line item (CLI), and this is thePatient CLI File. These additional pieces of information are added forease of data mining.

The other two input files for one embodiment of the present inventionare output files from EfficiencyCare system. One of these files is theDetail.tab File 68. A record in this file is the health care provider(e.g. physician).

The other file is called the ProvEP.tab File 44, which is an episodefile, and it contains all the final episodes of care that made itthrough EfficiencyCare system and into the Detail.tab file 68. In thisembodiment, the ProvEP.tab File 44 is preferred to have because itcontains the episode identifiers in this file that allow the presentinvention to tie back the Claim Line Items (CLIs) in the Patient CLIFile.

In one embodiment of the present invention the ProvEP.tab File 44 isused to identify the episode IDs for a health care provider, and it isthese episode IDs that are assigned to the health care provider (e.g.physician) and used to calculate his or her efficiency score. Then, thepresent invention data mines over into the PatientCLI.tab File 42 tofind out the CPT-4 codes responsible for a provider's 1.25 or 1.40efficiency score. That is, determining why the provider is using more orfewer services. However, there are hundreds of potential CPT-4 codesthat could be the cause, because a large number of different medicalconditions are typically being examined for each health care provider.So, the present invention uses a statistical measure, such as aPearson's Correlation (a statistic that associates two variables—in thiscase it is typically the health care provider's efficiency score fromEfficiencyCare system (other statistical tools, models, anddistributions are also within the scope of this invention)), to aprocedure or service (e.g., CPT-4 or HCPCS code) score. The closer to1.00, the stronger the association (with a Pearson's correlationcoefficient). So, the present invention typically reviews large numbersof potential procedure or service (e.g., CPT-4 or HCPCS) codes thatcould potentially be a primary cause of efficiency or inefficiency, andthen determines that a clinical leader should really just focus on asmall number (e.g. 2 to 5) of procedure or service codes because theseare the procedure or service codes that tend to be most associated withthose health care provider's efficiency scores that are high, forexample, 1.20 and above, or low. But, also note that these sameprocedure or service codes identify procedures that efficient providersare doing much less of. Thus, these MedMarkers (i.e., procedures orservice codes associated with provider efficiency scores) may also beused to identify efficient health care providers as well. This is whytypically MedMarkers are those procedures or services that areassociated with provider efficiency scores. And note that health careprovider efficiency scoring is preferably done on a specialty byspecialty basis, so cardiologists are evaluated separately from generalinternists and separately from pediatricians.

The present invention “automates” the process for targeting theseMedMarkers. That is, analysts at a health plan, physician group, or anyother organization might be able to look for these associations by doinga specialized three month study, and then determining the procedures andservices (e.g., CPT-4 and HCPCS) associated with the efficiency score ofhealth care providers for a specialty type. This is a long process. Thepresent invention provides software, methods, and algorithms thatautomate this process, greatly reducing the time needed to find theseassociations, as well as increasing the accuracy of the results.

After selecting the MedMarkers, the present invention then targets thehealth care providers that meet the specialty-specific practice patternas reflected by the MedMarkers selected by a user. It can then presentthe specified MedMarker results (rates per episode of care) for thehealth care provider as compared to the selected peer group.

The present invention saves information technology (IT) resources,statistician and analyst resources, and clinical resources needed by ahealth plan, physician group, or any other organization to identifythese important MedMarkers. The process is automated.

Also, once these MedMarkers are known, then the health plan, physiciangroup, or any other organization can take action (i.e., implementstrategies that fit each health plan, physician group, or any otherorganization's philosophies for reducing practice patterns variation) toimprove efficiency through working with the health care providers toreduce variability in the identified MedMarkers, focus health carepayment reform with respect to the MedMarkers, and implement health planbenefit plan design changes such as adding in deductibles or copaymentsfor the MedMarkers to make the consumer more aware of those services(i.e., MedMarkers) associated with inefficient health care providerpractice patterns.

The following personnel in a health plan or physician group can usethese MedMarkers to improve medical management performance: medicaldirectors to work with network health care providers to improveperformance; health care analysts and informatics specialists thatexamine claims data to observe reasons for health care provider practicepattern differences or variation; health care actuaries that want tounderstand services and procedures (i.e., MedMarkers) to target tochange health care provider reimbursement to reduce adverse incentivesfor health care providers to perform more of a certain service orprocedure.

One embodiment of the present invention utilizes ASCII tab-delimiteddatabase output files from the Grouper system and the EfficiencyCaresystem. There are the Detail.tab 68, PatientCLI.tab 42, and ProvEP.tab44 Files. Then, this embodiment, using these input files, produces twointermediate output files, ProvCLI.tab and MinProvEp.tab. Theseintermediate output files are then used to produce two final outputfiles, BullsEyeMB.tab and BullsEyeMCID.tab. Other file and datastructures are also within the scope of the present invention, includingdatabases.

The present invention is the first to use statistical techniques thatautomates the process for identifying only those procedures and services(e.g., CPT-4 and HCPCS codes) that are most associated with the healthcare provider efficiency score. One of the unexpected advantages is thatthe MedMarkers are often unexpected, and sometimes evencounter-intuitive.

Also, in other embodiments of the present invention:

-   -   In the preferred embodiment, only services and procedures are        analyzed. However, in another embodiment, drug prescriptions are        analyzed in a similar manner.    -   In another embodiment, there may be a spreadsheet that loads the        user identified MedMarkers, the MedMarker service rate per        episode, the targeted lower MedMarker rate per episode, the        average allowed charge amount for each MedMarker, and the        prevalence rate of the medical condition. The spreadsheet can        then calculate potential savings for the user using the below        formula:        Savings Calculation=Current MedMarker services per episode (−)        Target MedMarker services per episode (×) Average allowed charge        per service (×) Number of episodes    -   In another embodiment, Service Code Groups are built. In one        example, two unique CPT-4 codes for skin biopsy (11100        and 11101) may be examined separately, and therefore, perform a        Pearson's correlation on them separately. But, in another        embodiment, they are combined together into a specific Service        Code Group, which is this case can be called Skin        Biopsies=11100+11101. The rates per episode would also be        combined and the present invention would be run only after        Service Code Groups are formed to find MedMarkers. Here are some        possible Service Code Groups:        -   Destruction of Premalignant Lesions=17000+17004 (these are            two of several CPT-4 codes corresponding to Destruction of            Premalignant Lesions)        -   Shave Skin Lesions=11300+11301+11305+11310 (these are some            of the several CPT-4 codes corresponding to Shave Skin            Lesions)

Calculating the Pearson's Correlations, eventually, on Service CodeGroups in some situations may result in more meaningful results to auser than just inspecting each CPT-4 code result individually. Note thatthe CPT-4 codes in a Service Code Group often look very similar in termsof their verbal description—because they are. For example, under theDestruction of Premalignant Lesions, it may be that code 17000 is usedfor destroying fewer than 15 lesions, and code 17004 is used for billingpurposes for destroying more than 15 lesions. One can see on the verbaldescription for the codes that code 17004 has +15 lesions on it. Thus,these codes are very similar, and sometimes are just volume oriented.Here's another potential Service Code Group:

-   -   Upper Gastrointestinal (GI) Endoscopy=43239+43235 (these are two        of several CPT-4 codes corresponding to Upper GI Endoscopy),        whereby:        -   43239=Upper GI Endoscopy with biopsy        -   43239=Upper GI Endoscopy, diagnosis without biopsy            Thus, here, the determination is not made based on numbers,            but instead a moderate procedure type difference which is            having a biopsy present or not. However, this still would            potentially be a good Service Code Group.

One embodiment of the present invention is made up of four components:

-   -   The Grouper system groups unique ICD.9 diagnosis codes into 526        meaningful medical conditions based on clinical homogeneity with        respect to generating a similar clinical response from health        care providers treating a patient.    -   The EfficiencyCare system is health care provider efficiency        measurement software that takes the output from the Grouper        system and develops specialty-specific health care provider        efficiency scores that compare individual health care provider        efficiency against the efficiency of a peer group of interest or        practice pattern of interest.    -   Correlation Calculation Software takes output from the Grouper        system and EfficiencyCare system and performs correlation        analysis of health care providers' service, sub-service, and        procedure or service code scores as compared to their efficiency        score.    -   A Reporting Dashboard, Other Reports, and Open Architecture        Output Files. The Reporting Dashboard produces correlation        summary reports by service category, sub-service category, and        procedures and service code. Reports may include a MedMarker        Selection/Summary Report, and Health Care Provider Summary        Report. Embodiments of the present invention also provides other        reports at key points during processing. All reports are based        on output files accessible to the user, and these output files        may be used for additional client-developed analysis.

There are several ways that the present invention may be used to addvalue to an organization. The present invention rapidly targetsMedMarkers (i.e., those few procedures and services most associated withhealth care provider efficiency scores). Knowing these MedMarkers, thepresent invention identifies health care providers meeting anorganization's established MedMarker criteria. On drill-down, the usergenerally knows the established MedMarker practice patterns peridentified health care provider.

Next, users can identify a practice pattern (preferably per specialtytype) that identifies inefficient health care providers. Therefore, theymay develop and educate their medical management staff on a standard,MedMarker-based, practice pattern. This enables an organization'smedical management staff to cost-effectively implement and monitor onestandard health care provider feedback program.

Moreover, MedMarkers identified by the present invention identifypotential areas of significant procedure and service over-utilization,upcoding, and unbundling. Therefore, MedMarkers may serve as a sourcefor potential health care provider fee payment adjustments by specialtytype per region. Here are some examples:

-   -   Potential over-utilization example: Dermatologists receiving an        inefficient score perform more skin biopsies for the same        grouping of medical conditions.    -   Potential upcoding example: Dermatologists receiving an        inefficient score upcode their office visits from 10 minutes to        15-or-20 minutes.    -   Potential unbundling example: Dermatologists performing a skin        biopsy receive payment for both a 20 minute office visit and the        skin biopsy, increasing their payment over 300% as compared to a        10 minute office visit alone.        An organization now can have explicit procedures and services to        improve its current health care provider payment system by        implementing changes to reduce over-utilization, upcoding, and        unbundling.

Furthermore, health services research shows that health care providerefficiency measurement methodologies often falsely identify some healthcare providers as inefficient, when in fact, the health care providersreally are efficient (“false positives”). As a result, health careproviders may be inappropriately excluded from high performance networksor not receive pay for performance bonuses.

For the first time, organizations can have an automated tool to validatethe accuracy of their health care provider efficiency scores. In orderfor each health care provider's score to be validated as accurate, theycan confirm that the health care provider has a higher MedMarkerutilization per episode (as compared to the peer group). The end resultwill typically be higher acceptance of results by network health careproviders, thereby reducing potential conflicts, as well as reducing theclinical and analyst resources used to justify the accuracy of eachscore.

The present invention uses the output from Grouper and EfficiencyCaresystems to develop specialty-specific correlations to health careprovider efficiency at the:

-   -   Service and sub-service category level    -   Medical condition level    -   Procedure or service code level,

There are several steps to identifying a MedMarker (i.e. a procedure andservice correlated to health care provider efficiency scores):

-   -   Apply minimum episode criteria for health care providers to be        used in correlation analysis.    -   For each health care provider, calculate an overall weighted        average service and sub-service category score.    -   For each health care provider, create a medical        condition-specific service and sub-service category score.    -   Calculate an overall weighted average procedure or service code        score for each health care provider.    -   Calculate a medical condition-specific procedure or service code        score for each health care provider.    -   If desired, remove outlier health care providers from analysis        at a service category, sub-service category, and procedure or        service code level.    -   Calculate the correlation to health care provider efficiency        scores at each level described above using a Pearson's        correlation calculation.    -   Correlated service and sub-service categories and procedures or        services can be selected as MedMarkers and used to identify        health care providers that meet a desired practice pattern.

These steps preferably occur after removing outlier episodes and healthcare providers that did not meet a minimum episode number establishedwhen running EfficiencyCare system. Therefore, outlier episodesidentified during efficiency analysis, and health care providers notreceiving an efficiency score, are not included in the analysis.

In one embodiment, a health care provider must have a minimum number ofnon-outlier episodes at the specialty-specific marketbasket level ormedical condition level in order to be included in the correlationanalysis. This minimum episode number should not be confused with aminimum episode number used to establish whether a health care providerreceives an efficiency score.

In one embodiment, each health care provider's overall weighted averageservice category utilization per episode is divided by the peer groupoverall weighted average service category utilization per episode tocalculate an overall service category score. Also, each health careprovider's overall weighted average sub-service category utilization perepisode is divided by the corresponding peer group's overall weightedaverage sub-service category utilization per episode to calculate anoverall sub-service category score.

NOTE: Overall utilization rates for service and sub-service categoriesmay be found in the EfficiencyCare system output file: Detail.tab.

In one embodiment, CPT-4 and HCPCS codes represent the procedure orservice code level detail that is used to report services per episoderate for the health care provider and the peer group. The presentinvention uses this information at the overall weighted average level tocalculate a unique procedure or service code score. Each health careprovider's procedure or service code per episode rate is divided by thecorresponding peer group procedure or service code per episode rate tocalculate an overall procedure or service code score. For example, adermatologist's overall skin biopsy rate per episode may be 0.477services per episode. The peer group skin biopsy per episode rate is0.175, resulting in a CPT-4 score for the dermatologist of a0.477/0.175=2.72.

Similar to the overall weighted average service and sub-service categoryscore, a medical condition-specific service category and sub-servicecategory utilization score are calculated for each health care provider.Each health care provider's condition-specific service categoryutilization per episode is divided by the peer group service categoryutilization per episode to calculate a condition-specific servicecategory score. Also, each health care provider's condition-specificsub-service category utilization per episode is divided by thecorresponding peer group sub-service category utilization per episode tocalculate a condition-specific sub-service category score.

NOTE: Medical condition-specific utilization rates for service andsub-service categories may be found in the EfficiencyCare system outputfile: Detail.tab.

In one embodiment, CPT-4 and HCPCS code detail may also be available formedical conditions within a market basket of interest. Thecondition-specific services per episode rate for the health careprovider and the peer group may be used to calculate a service codescore. For a specific medical condition, each health care provider'sservice code per episode rate is divided by the corresponding peer groupcondition-specific service code per episode rate to calculate a score.For example, a dermatologist's benign neoplasm of the skin biopsy rateper episode may be 0.500 services per episode. The peer group benignneoplasm of the skin biopsy rate per episode may be 0.250, resulting ina CPT-4 score for the dermatologist of a 0.500/0.250=2.00.

In the preferred embodiment health care provider outlier analysis ispreferably performed after health care providers receive a servicecategory score. The parameter SWITCH_BE_PROVOUTLIER in the run.iniconfiguration file defines the percent of health care providers thatwill be removed from correlation analysis in one embodiment of thepresent invention. Within each specialty marketbasket's servicecategory, a percentage of health care providers with the greatestabsolute variance between the health care provider's efficiency scoreand the service category score are removed from correlation analysis inthis embodiment. The health care provider outlier analysis removeshealth care providers having differences that are far away from a majorpart of the data. One reason for removing them is that those health careprovider outliers in the “difference area” may not be reliable from astatistical sense. Typically, the same health care providers are removedfrom sub-service category and procedure or service codes within thecorresponding service category for both the overall marketbasket leveland medical condition level correlation analysis. The health careproviders included in the correlation analysis may differ by servicecategory. For example, the health care provider outlier parameterdefault value may be 10%. Table 1 refers to a General Internist with anoverall efficiency score of a 0.90, and demonstrates if this health careprovider is to be included in correlation analysis for two separateservice categories. In other embodiments, other health care provideroutlier analysis methods may be utilized.

TABLE 1 General Internist Physician Outlier Example Include Physician inCorrelation Overall Service Abso- Analysis? (includes Service Effi-Cate- lute corresponding sub-service cate- Cate- ciency gory Vari- goryand procedure or service gory Score Score ance level correlationanalysis) Diag- 0.90 2.50 1.60 No. This physician is in the nostic top10% of physicians with Tests greatest variance. Medical/ 0.90 1.20 0.30Yes. This physician is not in Surgical the top 10% of physicians withthe greatest variance.If the percent of health care providers removed as outliers cannot beachieved, then no health care providers are removed from the peer groupin one embodiment of the present invention. For example, if there are 6Allergists and 10% are to be removed, no health care providers areremoved from the Allergist marketbasket for correlation analysis.

Peer group substitution is preferably used for health care providers whohave passed the outlier criteria, but have not performed any services ina service category, sub-service category, or for a service code. Healthcare providers who did not receive a service category, sub-servicecategory, or procedure or service code score because they did notperform those services or procedures will receive a score of a 1.0,which represents the peer group results. For example, if a health careprovider did not perform any imaging tests, the health care provider'soverall weighted average sub-service category score for imaging wouldpreferably be substituted with a value of 1.0. In other embodiments,other peer group substitution methods may be utilized.

The main statistical analysis performed in one embodiment of the presentinvention is the Pearson's correlation analysis. Pearson's correlationanalysis is used to calculate the correlation of a service category,sub-service category, or procedure or service code to health careprovider efficiency score—Pearson's correlation coefficient (r). In thepresentation of the correlation results, the correlation coefficient (r)indicates the strength and direction of a linear relationship betweenthe dependant and independent variables, and varies from a low of −1.00to a high of 1.00. The higher the absolute value of the coefficient, thestronger the relationship between the two variables. In health servicesresearch, two variables may be considered fairly correlated if “r” isgreater than some limit (e.g., 0.20 or so). Also, two variables may beconsidered highly correlated if “r” is greater than some limit (e.g.,0.40 or so). Other statistical measurements are also within the scope ofthe present invention.

Correlation analysis is typically based on the identification of thedependent and independent variables which defines the detailed level foranalysis.

-   -   Dependent variable. The dependent variable in the correlation        model in the preferred embodiment of the present invention is a        health care provider's efficiency score. The dependent variable        is the health care provider's specialty-specific overall        weighted average efficiency score if looking at the overall        market basket level. The dependent variable is the health care        provider's specialty-specific and medical condition-specific        efficiency score if looking at the medical condition level.    -   Independent variables. There are three (3) types of independent        variables that are included in the preferred embodiment of the        present invention. These are listed in the following table.

TABLE 2 Potential Independent Variable Types Variable Types DefinitionService This is the service category score at either the overallCategory marketbasket level or the medical condition-specific level.Score In one embodiment, there are 11 service categories. Sub-ServiceThis is the sub-service category score at either the overall Categorymarketbasket level or the medical condition-specific level. Score In oneembodiment, there are 21 sub-service categories. Procedure or This is aprocedure or service code score at the overall Service Code marketbasketlevel or the medical condition-specific level. Score In one embodiment,the procedure or service code score is based on CPT-4 or HCPCS codes.

The Pearson's correlation coefficient (r) is used in one embodiment ofthe present invention to determine the strength of the relationshipbetween the health care provider efficiency score and health careprovider service category, sub-service category, and service code score.This coefficient provides a numeric measure of the strength of thelinear relationship between these two variables.

Pearson's correlation coefficient (r) ranges from a low of −1.00 to ahigh of 1.00. Positive correlations mean that the health care providerservice category, sub-service category, and service code scores arepositively associated with the health care provider efficiency score.That is, if a health care provider does more of the particular servicecode per episode as compared to the peer group, then the health careprovider most often has an efficiency score greater than a 1.00. Viceversa, if a health care provider does less of the particular servicecode per episode as compared to the peer group, then the health careprovider most often has an efficiency score less than a 1.00. Therefore,a positively correlated service code indicates that health careproviders performing more of this service code tend to have moreinefficient practice patterns as compared to the peer group. Negativecorrelations mean that the health care provider service category,sub-service category, and service code scores are negatively associatedwith the health care provider efficiency score. That is, if a healthcare provider does more of the particular service code per episode ascompared to the peer group, then the health care provider most often hasan efficiency score less than a 1.00. Vice versa, if a health careprovider does less of the particular service code per episode ascompared to the peer group, then the health care provider most often hasan efficiency score greater than a 1.00. Therefore, a negativelycorrelated service code indicates that health care providers performingmore of this service code tend to have more efficient practice patternsas compared to the peer group. Note that Pearson's correlationcoefficient is used in one embodiment of the present invention and isused here as an example of a measure of correlation. Other measures ofcorrelation are also within the scope of the present invention.

TABLE 3 Potential Correlation Intervals in Relation to EfficiencyCorrelation Range Correlation to Efficiency or Inefficiency  >0.40 Highpositive correlation to health care provider efficiency scores; the morehe does, the more likely the health care provider is to receive aninefficient score. 0.20 to 0.40 Good positive correlation to health careprovider efficiency scores −0.20 to 0.20  Low to no correlation tohealth care provider efficiency scores −0.20 to −0.40 Good negativecorrelation to health care provider efficiency scores <−0.40 Highnegative correlation to health care provider efficiency scores; the morehe does, the more likely the health care provider is to receive anefficient score.

FIG. 1 is a graph showing a Positive Correlation Example. In this FIG.,each procedure score for skin biopsies (CPT-4 11100) has been plottedagainst each dermatologist's overall health care provider efficiencyscore. When the CPT-4 score is high, the health care provider efficiencyscore is high. Alternatively, when the CPT-4 score is low, the overallefficiency score is low, resulting in a high Pearson's correlationcoefficient of a 0.64. According to Table 3 (above)—PotentialCorrelation Intervals in Relation to Efficiency, in this population,skin biopsies have a high positive correlation to health care providerefficiency scores, indicating a health care provider doing more of thisprocedure is more likely to receive an inefficient score.

FIG. 2 is a graph showing a Negative Correlation Example. In this FIG.,each procedure score for ECG Monitoring (CPT-4 93325) has been plottedagainst each Cardiologists' overall health care provider efficiencyscore. In this example, when the procedure score for ECG Monitoring(CPT-4 93325) for Cardiologists is high, the health care providerefficiency score is low. This is the opposite of the skin biopsy patternshown above. When the CPT-4 score is low, the overall efficiency scoreis high, resulting in a negative Pearson's correlation coefficient of a−0.26. According to Table 3 (above)—Potential Correlation Intervals inRelation to Efficiency, in this population, ECG Monitoring has a goodnegative correlation to health care provider efficiency scores,indicating a health care provider doing more of this procedure is morelikely to receive an efficient score.

A MedMarker is preferably a CPT-4 or HCPCS code that is relativelycorrelated to the health care provider efficiency score. To qualify as aMedMarker, the procedure or service should preferably have the followingproperties:

-   -   Good correlation (using Pearson's correlation “r” in this        example) to a health care provider specialty type's overall or        medical condition-specific efficiency score;    -   A higher prevalence rate per overall weighted average episode of        care, or medical condition-specific episode of care.    -   Clinical relevance in terms of medical support literature as to        when service should be performed; and    -   A reasonable charge per service (e.g., $50-to-$400 per service        in this example). The health care provider's condition-specific        efficiency score is a reflection of the services used to treat a        specific medical condition as compared to an immediate peer        group.    -   More than a given percentage of the health care providers        (within the specialty type of interest) perform one or more of        the service code of interest.

The present invention allows an organization to identify one mainpractice pattern per specialty type per region that is most associatedwith health care provider efficiency scores, and identify those healthcare providers who meet this practice pattern.

-   -   Identify a MedMarker (or several MedMarkers) that will be used        to establish a practice pattern for specialty-specific health        care providers in a given region (see FIG. 4). Users can select        positively or negatively correlated MedMarkers (see FIG. 1 &        FIG. 2).    -   For the MedMarkers selected, define the percentage above or        below the services per episode rate to identify health care        providers with a specified practice pattern. For example, for        MRI of the lumbar region (CPT-4 72148), a general internist's        service per episode rate should be 10% higher than the peer        group rate for the health care provider to be defined as meeting        the practice pattern (see FIG. 5).    -   When selecting multiple MedMarkers to establish a practice        pattern, a threshold can be set for the amount of MedMarkers        that must meet or exceed the services per episode rate. For        example, if 7 MedMarkers are used to establish a practice        pattern, a user may only require 5 out of 7 MedMarker services        per episode rate be met in order to identify a health care        provider as matching a specified practice pattern (see FIG. 5).

The present invention will preferably produce a list of Provider IDsthat match the identified practice pattern (see FIG. 5). The Provider IDlist produced by the present invention can be loaded into EfficiencyCarePractitioner Efficiency Reports to further drill down on their practicepatterns.

FIGS. 6 through 8 are diagrams illustrating the process of identifyingMedMarkers, in accordance with one embodiment of the present invention.These examples are exemplary, and are only included here forillustrative purposes. It should be understood that these functions areautomated in a computer system in a preferred embodiment of the presentinvention, and the separate reports are shown merely to illustrate theprocess.

FIG. 6 is an exemplary Practitioner Efficiency Report, in accordancewith one embodiment of the present invention. It contains episodeinformation for one practitioner (i.e. health care provider) for anumber of medical conditions. For each medical condition, as well as aweighted average of all such medical conditions, averages for a peergroup of health care providers are also shown. For each medicalcondition and weighted average, there are a number of columns. It showsthe average charges per episode of care. The first column shows the nameof the medical condition. The next shows a Severity of Illness (SOI)level for the condition. This is followed by an episode count andaverage charger per episode. Then, the average charge per episode isbroken down across service categories in columns for: professionalvisits; diagnostic tests; lab/pathology; medical/surgical; prescriptions(Rx); facility outpatient; facility hospital; alternate sites; and othermedical expenses. An efficiency score is computed for the practitionerby dividing his average charge per episode of his average weightedcharges by the average charge per episode for the peer group. In thiscase, the average charge per episode for this practitioner was $567, andfor the peer group, it was $399. The quotient of these two averagecharges is 1.42, which can be utilized as an efficiency measurement.Other methods of and techniques for computing an efficiency measurementor score for health care providers are also within the scope of thepresent invention. In comparing this health care provider with others,this efficiency rating is in the 4^(th) quartile, or 10^(th) decile. Aquestion is asked, what CPT-4 code is most associated with theefficiency score? There are several steps outlined below to answer thisquestion. The first step is to identify a service category where thehealth care provider has significantly higher overall weighted averagecharges than the peer group. In this example, medical/surgical overallweighted average charge for the health care provider is significantlyhigher than the overall weighted average charge for the peer group asindicated by the asterisk on the practitioner weighted average resultfor Med/Surg, and is circled to illustrate this.

A next step is to drill-down to the service code level under sub-serviceambulatory surgical procedures to identify health care provider servicecodes with higher per episode rates than the peer group. FIG. 7 is anexemplary Service Prevalence Report in accordance with the example shownin FIG. 6. For the Dermatology specialty the report contains informationon services ordered for one healthcare provider and the peer group. Thereport also shows the number of unique episodes for the healthcareprovider and the peer group as well as the number of unique healthcareproviders in the peer group. Also, for both the healthcare provider andthe peer group, the number of services, number of services per episode,and the charge per service is shown for each service listed. There isalso a column showing services per episode percent difference from thepeer group.

Next, there is also a CPT-4 table shown in FIG. 7 (in the upper righthand quadrant) for CPT-4 11100. In the CPT-4 11100 table, the overallefficiency scores of several healthcare providers are shown for thisCPT-4 code (biopsy, skin lesion). In one embodiment, it would containentries for each healthcare provider in the peer group having treated asufficient number of episodes in the Dermatology marketbasket of medicalconditions. Also, a CPT-4 score is calculated for this CPT-4 code bydividing a healthcare provider number of services per episode by anaverage value for his peer group. The CPT-4 score for each healthcareprovider in the table is included in a CPT-4 score column along side hisefficiency score. In the case of the first Dermatologist in the table,overall efficiency score is 1.42, and the first Dermatologist has aCPT-4 score of 2.73. In one embodiment, this type of CPT-4 table isgenerated for each CPT-4 code being evaluated as a potential MedMarker.After the CPT-4 table is populated for a CPT-4 code, a statisticalmeasurement, such as a correlation coefficient (e.g. Pearson's “r”), iscomputed for the pairs of efficiency scores and CPT-4 scores for eachrow in the table. In one embodiment, a Pearson's coefficient is thestatistical measurement calculated. In other embodiments, other measuresof correlation or other statistical measurements may be utilized.

Finally, to identify the CPT-4 code most associated with efficiencyscores for the Dermatologists, FIG. 8 provides an exemplary ProcedureCode Report for the Dermatology specialty type, in accordance with theinvention and example shown in FIG. 7. This report shows one line foreach CPT-4 code being evaluated as a potential MedMarker for the givensub-service category of ambulatory surgical services. One example is thePearson's correlation computed for CPT-4 11100 shown in FIG. 7. Thefirst column in the report contains the statistical measurement (e.g.Pearson's correlation coefficient) calculated for pairs of efficiencyscores and CPT-4 scores for that CPT-4 code. The second column containsthe corresponding CPT-4 procedure code. This is followed by columns fora short name for the CPT-4 code, an average rate per episode for thiscode, and an average cost per procedure. The CPT-4 codes withsufficiently high positive or negative correlations are considered asMedMarkers. In this FIG. 8, CPT-4 procedure 11100 has a correlation of0.289, 11101 has a correlation of 0.218, 11401 has a correlation of0.302, and 11402 has a correlation coefficient of 0.221. These all havea correlation coefficient greater than 0.2, which is a exemplary cutoffin one implementation of the present invention, and these services,therefore, may be considered as potential MedMarkers. They all have arelatively high correlation between efficiency scores and CPT-4 scores.The remainder of the CPT-4 codes listed for this sub-service categoryhave lower correlation coefficients, are thus less correlated, and arepreferably eliminated from consideration as potential MedMarkers.

The MedMarker information presented in FIG. 8 is for sub-servicecategory of ambulatory surgical services across all medical conditionsin the Dermatology marketbasket. In one embodiment, MedMarkers can beidentified across all sub-service category services for a given medicalcondition (see FIG. 3) FIG. 3 is an exemplary Sub-Service DetailCorrelation Report, in accordance with one embodiment of the presentinvention. This report shows the correlation between different servicesand health care provider efficiency for a specialty (in this example,General Internist) and a specific medical condition (in this example,Low back pain). The fields in this report are:

Field Name Notes Top of Report Marketbasket This is the name of thespecialty-specific marketbasket selected for analysis. SOI - Medical Aspecialty-specific marketbasket consists of the common medical Conditionconditions treated by each specialty type. This field presents themedical condition name and the severity-of-illness (SOI) being examined.There are up to three SOI levels for each medical condition, with SOI-1being the least severe (routine, non-complicated), and SOI-3 being themost severe SOI (severity of illness). Aggregate Group MarketbasketSystem output contains information organized by aggregate groups thatusers define. This is the name of the aggregate group relevant to thecurrent data run. Correlation Cutoff This is the cutoff value used todetermine what procedures or services to display on the Sub-ServiceDetail Report. This parameter is not applicable for the service categoryand sub-service category level reports. Body of Report Columns [Shoppingcart This column allows you to select the MedMarkers of interest to addto a icon] user's BullsEye “shopping cart”. Any service category orsub-service category row can be selected by checking the box under thiscolumn. Corr This column presents the correlation results of the servicecategories or sub-service categories to the health care provider'sefficiency scores at the overall marketbasket level or the medicalcondition level within a marketbasket. Service/Sub- This column presentsthe name of the 11 service categories or 21 sub- Service Categoryservice categories. Number Services This column presents the totalnumber of services for the specialty-specific peer group at the overallmarketbasket level or the medical condition level within a marketbasket.Service Units This column presents the type of service associated witheach service category or sub-service category. For example,“Professional Visits” service type is office visits. Services per Thiscolumn presents the average number of services per episode for theEpisode specialty-specific peer group at the overall marketbasket levelor the medical condition level. Charge per Service This column presentsthe average charge per service for the specialty-specific peer group atthe overall marketbasket level or the medical condition level within amarketbasket. Unique This column presents the number of health careproviders in the specialty- Practitioners specific peer group at theoverall marketbasket level or the medical condition level within amarketbasket. Performing This column presents the percentage of thehealth care providers in the Practitioners peer group having performedthe service at least once at the overall marketbasket or the medicalcondition level.

As defined earlier in discussion of FIG. 5, FIG. 4 shows an exemplaryMedMarker Checkout Report, in accordance with the embodiment shown inFIG. 3. This report is a subset of the report shown in FIG. 3, withcolumns from that report selected by clicking under the marketbasketicon (

) in the first column.

FIG. 5 shows a MedMarker Target Report, in accordance with theembodiment shown in FIG. 4 as discussed earlier. A user first selects anumber of services as show in FIG. 4 by clicking under the marketbasketicon (

) in FIG. 3. The user then selects how many of the marketbasket servicesare required for a health care provider in this report (the report shownrequires one of the three) and a threshold based on the peer group. Thereport generated lists the practitioners who qualify under thesecriteria. The fields in this report are:

Field Name Notes Top of Report Aggregate Group Marketbasket Systemoutput contains information organized by aggregate groups that youdefine. This is the name of the aggregate group relevant to the currentdata run. Marketbasket This is the name of the specialty-specificmarketbasket selected for analysis. Medical Condition Aspecialty-specific marketbasket consists of the common medicalconditions treated by each health care provider specialty type. Thisfield presents the medical condition name being examined. If analysis isperformed at the marketbasket level, this field will contain the value“all”. SOI This field presents the severity-of-illness (SOI) beingexamined. There are up to three SOI levels for each medical condition,with SOI-1 being the least severe (routine, non-complicated), and SOI-3being the most severe SOI (severity of illness). If analysis isperformed at the Marketbasket level, this field will be blank. Body ofReport Columns Practitioner ID This is the unique identification numberassigned to the health care provider analyzed. Practitioner Name This isthe name of the health care provider analyzed. Efficiency Score This isthe efficiency score for each health care provider. At the marketbasketlevel, the efficiency score is calculated by dividing the health careprovider's weighted average overall charges by the specialty-specificpeer group's weighted average overall charges. At the medical condition-SOI level, the efficiency score is calculated by dividing the healthcare provider's average medical condition-SOI charges by thespecialty-specific peer group's average medical condition-SOI chargeswithin a marketbasket. MedMarkers This column presents the number ofMedMarker criteria met by each Meeting Criteria health care provider.

A Practitioner MedMarker Report (not shown) provides users withadditional detailed information for each health care provider displayedin the MedMarker Target Report shown in FIG. 5. The MedMarker Targetreport has links for each practitioner, and when that link is selected,the details for each of the selected MedMarkers is shown for thatpractitioners.

FIGS. 9 and 10 are flowcharts illustrating exemplary operation of oneembodiment of the present invention. They are separated into twoflowcharts for illustrative purposes, and it should be understood thatthey may not be separate in different embodiments. Furthermore, filesare shown in these flowcharts. It should be understood this isillustrative and that other methods and techniques of data organizationand management are also within the scope of the present invention. Forexample, many of the operations shown may be implemented throughdatabase operations in place of file operations.

FIG. 9 starts by reading in a PatientCLI file 42, a ProvEp file 44, andRun.ini parameters 40. From these files, claims data fields areextracted for scored health care providers, step 46. From this, aProvider CLI assignment structure file is built, step 48, and a ReducedProvider Episode Structure Output file is built, step 50. Then, thefirst phase of files are written, step 50, including a ProvCLI file 54,a BullsEye file 56, and a MinProvEp file 58.

FIG. 10 starts by reading in clinical tables, step 70 from a MedCondfile 62, Specmb file 64, and MBConditions file 66. Also, data files areread in, step 68, including: a Detail file 68; the ProvCLI file 54; andthe MinProvEp file 58. The Run.ini run time parameters 40 are read in,and a sort is performed, step 72. A loop is entered, starting withreading a single ProvCLI record, step 74. Health care providers areaggregated to the peer group level, step 76. Claim Line Items areaggregated for service and subservice categories at the peer grouplevel, step 78 and at the provider level, step 80. An inner loop repeatsfor each CLI record, step 74. Then, data is prepared for statisticalanalysis, step 80, and statistical analysis, such as Pearson'scorrelation, is performed, step 84. Service and subservice categoryprovider and peer group records are written, step 86, and provider andpeer group records are written, step 88. An outer loop then repeats,starting at the beginning of the CLI records, step 74. At the end of theouter loop, the output files are written, step 90, including: aBullsEyeMB.tab file 92; a BullEyeMCID.tab file 94; a BullsEyeMB.txt file96; and a BullsEyeMCID.txt file 98.

FIG. 11 is a block diagram illustrating a General Purpose Computer, suchas utilized to implement the present invention, as shown in FIGS. 9 and10. The General Purpose Computer 20 has a Computer Processor 22 (CPU),and Memory 24, connected by a Bus 26. Memory 24 is a relatively highspeed machine readable medium and includes Volatile Memories such asDRAM, and SRAM, and Non-Volatile Memories such as, ROM, FLASH, EPROM,EEPROM, and bubble memory. Also connected to the Bus are SecondaryStorage 30, External Storage 32, output devices such as a monitor 34,input devices such as a keyboard 36 with a mouse 37, and printers 38.Secondary Storage 30 includes machine-readable media such as hard diskdrives, magnetic drum, and bubble memory. External Storage 32 includesmachine-readable media such as floppy disks, removable hard drives,magnetic tape, CD-ROM, and even other computers, possibly connected viaa communications line 28. The distinction drawn here between SecondaryStorage 30 and External Storage 32 is primarily for convenience indescribing the invention. As such, it should be appreciated that thereis substantial functional overlap between these elements. Computersoftware such operating systems, utilities, user programs, and softwareto implement the present invention and data files can be stored in aComputer Software Storage Medium, such as memory 24, Secondary Storage30, and External Storage 32. Executable versions of computer software33, such as software utilized to implement the present invention can beread from a Non-Volatile Storage Medium such as External Storage 32,Secondary Storage 30, and Non-Volatile Memory and loaded for executiondirectly into Volatile Memory, executed directly out of Non-VolatileMemory, or stored on the Secondary Storage 30 prior to loading intoVolatile Memory for execution.

Those skilled in the art will recognize that modifications andvariations can be made without departing from the spirit of theinvention. Therefore, it is intended that this invention encompass allsuch variations and modifications as fall within the scope of theappended claims.

What is claimed is:
 1. A computer-implemented method of identifying apractice pattern associated with an efficiency of medical careproviders, said method comprising: providing, at a computer system, aplurality of episodes of care records, wherein each of the plurality ofepisodes of care records is identified by one of a plurality ofepisode-of-care identifiers, and includes one of a plurality of provideridentifiers identifying one of a plurality of medical care providers;providing, at the computer system, a plurality of patient treatmentclaim records, wherein each of the plurality of patient treatment claimrecords includes (i) an associated one of the episode-of-careidentifiers, and (ii) at least one of a plurality of codes, each of theplurality of codes associated with at least one of a procedure andservice in a medical care field associated with the plurality of medicalcare providers; providing, at the computer system, a plurality ofmedical care provider overall efficiency measurements each associatedwith one of the plurality of provider identifiers; calculating, at thecomputer system for each of the plurality of medical care providersusing the episodes of care records for the respective medical careprovider and the corresponding patient treatment claim records, aprovider rate of utilization for each of the codes; calculating, at thecomputer system using the episodes of care records across the pluralityof medical care providers and the corresponding patient treatment claimrecords, a group rate of utilization for each of the codes; associating,by the computer system, with each of the plurality of medical careproviders, a code score for each of the codes, wherein the code score isbased on (i) the provider rate of utilization for the respective codeand the respective medical care provider relative to (ii) the group rateof utilization for the respective code; calculating, for each of thecodes, an association value based on a plurality of pairs of values,each pair of values corresponding to a respective one of the pluralityof medical care providers and comprising: the overall efficiencymeasurement associated with the respective medical care provider, andthe code score associated with the respective code for the respectivemedical care provider; displaying, to a user, a code report including(i) an identifier of at least a portion of the codes, and (ii) theassociation value corresponding to each included code; receiving, fromthe user, a designation of a set of the included codes as the practicepattern associated with efficiency; and displaying, to the user, aprovider target report, wherein the provider target report includes (i)a respective identifier of each of a subset of the plurality of medicalcare providers, (ii) for each medical care provider in the subset, anidentifier of a number of the designated codes in the practice patternfor which the provider rate of utilization exceeds the group rate ofutilization by a second threshold amount, and (iii) for each medicalcare provider in the subset, a link to a detail display of the providerrate of utilization for the included codes in the practice pattern. 2.The method in claim 1, wherein the provider rate of utilization and thegroup rate of utilization are calculated with respect to episodes of aspecified medical condition.
 3. The method in claim 1, wherein theprovider rate of utilization and the group rate of utilization arecalculated with respect to episodes of a set of medical conditionsassociated with a practice category associated with the plurality ofmedical care providers.
 4. The method in claim 1, wherein the providerrate of utilization and the group rate of utilization are based on apercentage of episodes of care of a specified medical condition in whichthe code was utilized.
 5. The method in claim 1 wherein the providerrate of utilization and the group rate of utilization are based on apercentage of episodes of care associated with a set of medicalconditions in which the code was utilized, the set of medical conditionsassociated with a practice category of the plurality of medical careproviders.
 6. The method in claim 1 wherein the provider rate ofutilization and the group rate of utilization are based on a totalnumber of instances of utilization of the code per a specified number ofepisodes of care of a specified medical condition.
 7. The method inclaim 1 wherein the provider rate of utilization and the group rate ofutilization are based on a total number of instances of utilization ofthe code per a specified number of episodes of care of a set of medicalconditions associated with a practice category of the plurality ofmedical care providers.
 8. The method in claim 1 wherein the providerrate of utilization and the group rate of utilization are based on atotal cost of utilization of the code per a specified number of episodesof care of a specified medical condition.
 9. The method in claim 1wherein the provider rate of utilization and the group rate ofutilization are based on a total cost of utilization of the code per aspecified number of episodes of care of a set of medical conditionsassociated with a practice category of the plurality of medical careproviders.
 10. The method in claim 1 wherein the provider rate ofutilization and the group rate of utilization are based on a totalnumber of instances of utilization of the code per number of patients.11. The method in claim 1 wherein the provider rate of utilization andthe group rate of utilization are based on a total cost of utilizationof the code per number of patients.
 12. The method in claim 1 whereinthe provider rate of utilization and the group rate of utilization arebased on a total number of instances of utilization of the code pernumber of members.
 13. The method in claim 1 wherein the provider rateof utilization and the group rate of utilization are based on a totalcost of utilization of the code per number of members.
 14. The method inclaim 1 wherein at least one medical care provider of the plurality ofmedical care providers is an individual practitioner.
 15. The method inclaim 1 wherein at least one medical care provider of the plurality ofmedical care providers is an aggregation of individual practitioners.16. The method in claim 15 wherein the aggregation of individualpractitioners is associated with a health plan.
 17. The method in claim15 wherein the aggregation of individual practitioners is associatedwith a geographic region.
 18. The method in claim 1 wherein theassociation value is a Pearson's correlation.
 19. The method in claim 18wherein the threshold is 0.2.
 20. The method in claim 18 wherein thethreshold is 0.4.
 21. The method in claim 1 wherein the associationvalue is a Spearman's rank-order correlation.
 22. The method in claim 1wherein the association value is a percent difference.
 23. The method inclaim 1, further comprising determining that the at least portion of thecodes has each been performed by at least a minimum percentage ofmedical care providers in a practice category associated with theplurality of medical care providers, prior to the step of displaying, tothe user, the code report including the identifier of the at leastportion of the codes.
 24. The method in claim 23 wherein the minimumpercentage is 30 percent.
 25. The method in claim 1 wherein each of thecodes comprises one or more procedure codes or service codes in adesignated medical care field.
 26. The method in claim 1 wherein theplurality of codes numbers more than
 100. 27. The method in claim 1wherein the plurality of codes numbers more than
 300. 28. The method inclaim 1 wherein the plurality of episodes of care records numbers atleast 10,000.
 29. The method in claim 28 wherein the plurality ofmedical care providers numbers at least
 90. 30. A computer system foridentifying a practice pattern associated with an efficiency of medicalcare providers, said computer system comprising: a processor capable ofexecuting computer instructions; a memory coupled to the processorcontaining computer instructions for: accessing a plurality of episodesof care records, wherein each of the plurality of episodes of carerecords is identified by one of a plurality of episode-of-careidentifiers, and includes one of a plurality of provider identifiersidentifying one of a plurality of medical care providers; accessing aplurality of patient treatment claim records, wherein each of theplurality of patient treatment claim records includes (i) an associatedone of the episode-of-care identifiers, and (ii) at least one of aplurality of codes, each of the plurality of codes associated with atleast one of a procedure and service in a medical care field associatedwith the plurality of medical care providers; accessing a plurality ofmedical care provider overall efficiency measurements each associatedwith one of the plurality of provider identifiers; calculating, for eachof the plurality of medical care providers using the episodes of carerecords for the respective medical care provider and the correspondingpatient treatment claim records, a provider rate of utilization for eachof the codes; calculating, using the episodes of care records across theplurality of medical care providers and the corresponding patienttreatment claim records, a group rate of utilization for each of thecodes; associating, with each of the plurality of medical careproviders, a code score for each of the codes, wherein the code score isbased on (i) the provider rate of utilization for the respective codeand the respective medical care provider relative to (ii) the group rateof utilization for the respective code; calculating, for each of thecodes, an association value based on a plurality of pairs of values,each pair of values corresponding to a respective one of the pluralityof medical care providers and comprising: the overall efficiencymeasurement associated with the respective medical care provider, andthe code score associated with the respective code for the respectivemedical care provider; displaying, to a user, a code report including(i) an identifier of at least a portion of the codes, and (ii) theassociation value corresponding to each included code; receiving, fromthe user, a designation of a set of the included codes as the practicepattern associated with efficiency; and displaying, to the user, aprovider target report, wherein the provider target report includes (i)a respective identifier of each of a subset of the plurality of medicalcare providers, (ii) for each medical care provider in the subset, anidentifier of a number of the designated codes in the practice patternfor which the provider rate of utilization exceeds the group rate ofutilization by a second threshold amount, and (iii) for each medicalcare provider in the subset, a link to a detail display of the providerrate of utilization for the included codes in the practice pattern. 31.A non-transitory computer-readable medium containing computerinstructions for identifying a practice pattern associated with anefficiency of medical care providers, said computer instructions whenexecuted by at least one processor cause the at least one processor toperform steps of: accessing a plurality of episodes of care records,wherein each of the plurality of episodes of care records is identifiedby one of a plurality of episode-of-care identifiers, and includes oneof a plurality of provider identifiers identifying one of a plurality ofmedical care providers; accessing a plurality of patient treatment claimrecords, wherein each of the plurality of patient treatment claimrecords includes (i) an associated one of the episode-of-careidentifiers, and (ii) at least one of a plurality of codes, each of theplurality of codes associated with at least one of a procedure andservice in a medical care field associated with the plurality of medicalcare providers; accessing a plurality of medical care provider overallefficiency measurements each associated with one of the plurality ofprovider identifiers; calculating, for each of the plurality of medicalcare providers using the episodes of care records for the respectivemedical care provider and the corresponding patient treatment claimrecords, a provider rate of utilization for each of the codes;calculating, using the episodes of care records across the plurality ofmedical care providers and the corresponding patient treatment claimrecords, a group rate of utilization for each of the codes; associating,with each of the plurality of medical care providers, a code score foreach of the codes, wherein the code score is based on (i) the providerrate of utilization for the respective code and the respective medicalcare provider relative to (ii) the group rate of utilization for therespective code; calculating, for each of the codes, an associationvalue based on a plurality of pairs of values, each pair of valuescorresponding to a respective one of the plurality of medical careproviders and comprising: the overall efficiency measurement associatedwith the respective medical care provider, and the code score associatedwith the respective code for the respective medical care provider;displaying, to a user, a code report including (i) an identifier of atleast a portion of the codes, and (ii) the association valuecorresponding to each included code; receiving, from the user, adesignation of a set of the included codes as the practice patternassociated with efficiency; and displaying, to the user, a providertarget report, wherein the provider target report includes (i) arespective identifier of each of a subset of the plurality of medicalcare providers, (ii) for each medical care provider in the subset, anidentifier of a number of the designated codes in the practice patternfor which the provider rate of utilization exceeds the group rate ofutilization by a second threshold amount, and (iii) for each medicalcare provider in the subset, a link to a detail display of the providerrate of utilization for the included codes in the practice pattern.